{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import r2_score   #评价回归预测模型的的性能\n",
    "import matplotlib.pyplot as plt    #画图\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取数据\n",
    "#path to where the data lies\n",
    "dpath = 'D:/0CSDN/机器学习-人工智能直通车/homework/4th week 线性回归/'\n",
    "data = pd.read_csv(dpath + \"day.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据准备"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据去噪"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除y大于等于50的样本（保留小于50的样本）\n",
    "#df = df[df.cnt< 50]\n",
    "\n",
    "#输出样本数和特征维数\n",
    "#print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#从原始数据中分离输入特征x和输出y\n",
    "y = data['cnt'].values\n",
    "X = data.drop('cnt', axis = 1)\n",
    "\n",
    "#尝试对y（骑行量）做log变换，对log变换后的值进行估计\n",
    "log_y = np.log1p(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 题目1.对连续型特征，可以用seabon 和matplotlib 可视化其分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#单变量分析分布\n",
    "#目标y(给定日期总租车人数）的直方图/分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values,bins = 30,kde = True)\n",
    "plt.xlabel('Values of bicycle cnt',fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 题目2.对于两个连续型特征，可以用corr得到两个特征的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17be4710>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = data.corr()                          #矩阵\n",
    "sns.heatmap(data_corr,annot = True)             #热力分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 题目3：特征之间存在强相关性，在选择线性回归模型时应该进行PCA降维（特征层面）或者加正则项（模型层面）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 去除干扰项"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop(\"dteday\",axis = 1)\n",
    "#X = X.drop(\"hum\",axis = 1)\n",
    "X = X.drop(\"season\",axis = 1)\n",
    "X = X.drop(\"yr\",axis = 1)\n",
    "X = X.drop(\"mnth\",axis = 1)\n",
    "X = X.drop(\"holiday\",axis = 1)\n",
    "#X = X.drop(\"weekday\",axis = 1)\n",
    "#X = X.drop(\"workingday\",axis = 1)\n",
    "X = X.drop(\"weathersit\",axis = 1)\n",
    "X = X.drop(\"casual\",axis = 1)                 ###丢弃类别型数值；casual、registered与cnt关联性较大，为避免对结果预测造成干扰，也进行丢弃。\n",
    "X = X.drop(\"registered\",axis = 1)\n",
    "#用于后续显示权重系数对应的特征\n",
    "columns = X.columns\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 题目4： 数值特征标准化（去量纲化）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#分别初始化对特征和目标值的标准化器\n",
    "ss_x = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "\n",
    "#分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "#对训练数据，先调用fit方法训练模型，得到模型参数；然后对训练数据和测试数据进行transform\n",
    "X = ss_x.fit_transform(X)\n",
    "\n",
    "y = ss_y.fit_transform(y.reshape(-1,1))\n",
    "log_y = ss_y.fit_transform(log_y.reshape(-1,1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分割测试集和训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(584, 7)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=3, test_size=0.2)     #random_state 即random_seed\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 最小二乘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>atemp</td>\n",
       "      <td>[1.1768157041616427]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>[0.4086055389820882]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>weekday</td>\n",
       "      <td>[0.040282674803133134]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>workingday</td>\n",
       "      <td>[0.01803995094196653]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.17363828963688677]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.3158980922591236]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>temp</td>\n",
       "      <td>[-0.5643466241349118]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      columns                    coef\n",
       "4       atemp    [1.1768157041616427]\n",
       "0     instant    [0.4086055389820882]\n",
       "1     weekday  [0.040282674803133134]\n",
       "2  workingday   [0.01803995094196653]\n",
       "6   windspeed  [-0.17363828963688677]\n",
       "5         hum   [-0.3158980922591236]\n",
       "3        temp   [-0.5643466241349118]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "#训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "#预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score iof Linear_Regression on test is  0.6743295501456223\n",
      "The r2 score iof Linear_Regression on train is  0.7500052038879497\n"
     ]
    }
   ],
   "source": [
    "print( 'The r2 score iof Linear_Regression on test is ',  r2_score(y_test, y_test_pred_lr))\n",
    "print('The r2 score iof Linear_Regression on train is ', r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L2正则-->岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "#alphas = [0.01, 0.1, 1, 10, 100]\n",
    "alphas = [ 0.0186,0.0187,0.0188, 0.0189]\n",
    "\n",
    "#生成RidgeCV实例：\n",
    "ridge = RidgeCV(alphas = alphas, store_cv_values=True)\n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0188"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score iof Linear_Regression on test is  0.6916472802241432\n",
      "The r2 score iof Linear_Regression on train is  0.7498549371377365\n"
     ]
    }
   ],
   "source": [
    "print( 'The r2 score iof Linear_Regression on test is ',  r2_score(y_test, y_test_pred_ridge))\n",
    "print('The r2 score iof Linear_Regression on train is ', r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>atemp</td>\n",
       "      <td>[1.0076474860990663]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>[0.40944715559271927]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>weekday</td>\n",
       "      <td>[0.03990975828471477]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>workingday</td>\n",
       "      <td>[0.01812947519960062]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.17874162624880086]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.31346977872239123]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>temp</td>\n",
       "      <td>[-0.40699043167156645]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      columns                    coef\n",
       "4       atemp    [1.0076474860990663]\n",
       "0     instant   [0.40944715559271927]\n",
       "1     weekday   [0.03990975828471477]\n",
       "2  workingday   [0.01812947519960062]\n",
       "6   windspeed  [-0.17874162624880086]\n",
       "5         hum  [-0.31346977872239123]\n",
       "3        temp  [-0.40699043167156645]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L1正则--> Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1100: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:471: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.02940236950180619, tolerance: 0.0018839161007839409\n",
      "  tol, rng, random, positive)\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:471: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.09888852192325137, tolerance: 0.0020088448853759463\n",
      "  tol, rng, random, positive)\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:471: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.025476951252078273, tolerance: 0.001946078947470273\n",
      "  tol, rng, random, positive)\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.06296849002856852, tolerance: 0.0029278700159256514\n",
      "  positive)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#alphas = [0.01, 0.1, 1, 10, 100]\n",
    "alphas = [ 0.000005991, 0.000005995, 0.000005997, 0.000005998,0.000005999]\n",
    "\n",
    "\n",
    "lasso = LassoCV(alphas = alphas)\n",
    "\n",
    "#训练\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.999e-06"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score iof Linear_Regression on test is  0.6794201403157121\n",
      "The r2 score iof Linear_Regression on train is  0.749993685956249\n"
     ]
    }
   ],
   "source": [
    "print( 'The r2 score iof Linear_Regression on test is ',  r2_score(y_test, y_test_pred_lasso))\n",
    "print('The r2 score iof Linear_Regression on train is ', r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>lr_coef</th>\n",
       "      <th>ridge_coef</th>\n",
       "      <th>lasso_coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>atemp</td>\n",
       "      <td>1.130180</td>\n",
       "      <td>[1.0076474860990663]</td>\n",
       "      <td>1.130180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>0.408814</td>\n",
       "      <td>[0.40944715559271927]</td>\n",
       "      <td>0.408814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>weekday</td>\n",
       "      <td>0.040136</td>\n",
       "      <td>[0.03990975828471477]</td>\n",
       "      <td>0.040136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>workingday</td>\n",
       "      <td>0.018037</td>\n",
       "      <td>[0.01812947519960062]</td>\n",
       "      <td>0.018037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-0.174812</td>\n",
       "      <td>[-0.17874162624880086]</td>\n",
       "      <td>-0.174812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>hum</td>\n",
       "      <td>-0.315046</td>\n",
       "      <td>[-0.31346977872239123]</td>\n",
       "      <td>-0.315046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>temp</td>\n",
       "      <td>-0.520885</td>\n",
       "      <td>[-0.40699043167156645]</td>\n",
       "      <td>-0.520885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      columns   lr_coef              ridge_coef  lasso_coef\n",
       "4       atemp  1.130180    [1.0076474860990663]    1.130180\n",
       "0     instant  0.408814   [0.40944715559271927]    0.408814\n",
       "1     weekday  0.040136   [0.03990975828471477]    0.040136\n",
       "2  workingday  0.018037   [0.01812947519960062]    0.018037\n",
       "6   windspeed -0.174812  [-0.17874162624880086]   -0.174812\n",
       "5         hum -0.315046  [-0.31346977872239123]   -0.315046\n",
       "3        temp -0.520885  [-0.40699043167156645]   -0.520885"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"lr_coef\":list((lasso.coef_.T)),\"ridge_coef\":list((ridge.coef_.T)),\"lasso_coef\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['lr_coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 题目5：不懂"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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